Evaluation of the modern visual SLAM methods

Huletski, Arthur; Kartashov, Dmitriy; Krinkin, Kirill

doi:10.1109/ainl-ismw-fruct.2015.7382963

Cited by 33 publications

(21 citation statements)

References 10 publications

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“…Visual SLAM approaches have been evaluated for indoor and outdoor applications over benchmark datasets. In [ 13 ] ORB-SLAM, Large Scale Direct SLAM (LSD-SLAM), Low dimensionality SLAM (L-SLAM) and open source of RatSLAM algorithms are briefly described and assessed. ORB-SLAM shows good results for different environments presenting the smallest errors when compared to LSD-SLAM and Rat-SLAM.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Several Feature Detectors/Extractors on Underwater Images towards vSLAM

Hidalgo

Bräunl

2020

Sensors

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Modern visual SLAM (vSLAM) algorithms take advantage of computer vision developments in image processing and in interest point detectors to create maps and trajectories from camera images. Different feature detectors and extractors have been evaluated for this purpose in air and ground environments, but not extensively for underwater scenarios. In this paper (I) we characterize underwater images where light and suspended particles alter considerably the images captured, (II) evaluate the performance of common interest points detectors and descriptors in a variety of underwater scenes and conditions towards vSLAM in terms of the number of features matched in subsequent video frames, the precision of the descriptors and the processing time. This research justifies the usage of feature detectors in vSLAM for underwater scenarios and present its challenges and limitations.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Several Feature Detectors/Extractors on Underwater Images towards vSLAM

Hidalgo

Bräunl

2020

Sensors

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“…On the other hand, monocular VSLAM methods that simultaneously recover camera pose and scene structure from video can be divided into two classes [ 46 ]: (a) feature-based methods, that firstly extract a set of feature observations from the image, and then compute the camera position and scene geometry as a function of these feature observations and (b) direct methods (dense or semi-dense), that optimize the geometry directly on the image’s pixels intensities, which enables using all information in the image.…”

Section: Slam Methods Descriptionmentioning

confidence: 99%

A Multi-Sensorial Simultaneous Localization and Mapping (SLAM) System for Low-Cost Micro Aerial Vehicles in GPS-Denied Environments

López

Garcı́a

Barea

et al. 2017

Sensors

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One of the main challenges of aerial robots navigation in indoor or GPS-denied environments is position estimation using only the available onboard sensors. This paper presents a Simultaneous Localization and Mapping (SLAM) system that remotely calculates the pose and environment map of different low-cost commercial aerial platforms, whose onboard computing capacity is usually limited. The proposed system adapts to the sensory configuration of the aerial robot, by integrating different state-of-the art SLAM methods based on vision, laser and/or inertial measurements using an Extended Kalman Filter (EKF). To do this, a minimum onboard sensory configuration is supposed, consisting of a monocular camera, an Inertial Measurement Unit (IMU) and an altimeter. It allows to improve the results of well-known monocular visual SLAM methods (LSD-SLAM and ORB-SLAM are tested and compared in this work) by solving scale ambiguity and providing additional information to the EKF. When payload and computational capabilities permit, a 2D laser sensor can be easily incorporated to the SLAM system, obtaining a local 2.5D map and a footprint estimation of the robot position that improves the 6D pose estimation through the EKF. We present some experimental results with two different commercial platforms, and validate the system by applying it to their position control.

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“…A mono-camera tracking algorithm could achieve real-time and accurate performance on a normal laptop with no GPU. For instance, ORB-SLAM reports an error of 1% [32]. Also, the camera orientation is built in the SLAM output.…”

Section: Research Deficiency and Challengementioning

confidence: 99%

Ground-Level Mapping and Navigating for Agriculture Based on IoT and Computer Vision

Zhao

Wang

et al. 2020

IEEE Access

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Evaluation of the modern visual SLAM methods

Cited by 33 publications

References 10 publications

Evaluation of Several Feature Detectors/Extractors on Underwater Images towards vSLAM

Evaluation of Several Feature Detectors/Extractors on Underwater Images towards vSLAM

A Multi-Sensorial Simultaneous Localization and Mapping (SLAM) System for Low-Cost Micro Aerial Vehicles in GPS-Denied Environments

Ground-Level Mapping and Navigating for Agriculture Based on IoT and Computer Vision

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